Data Science for Business

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# ER014 - Data Science & Strategy for Business

## PVA3

### Teil 2: Fallbeispiel

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### FS 2025
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### Prof. Dr. Jörg Schoder
.mycontacts[
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]

---
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<div style="position: absolute;left:400px;bottom:10px;font-size:9px"><svg aria-hidden="true" role="img" viewBox="0 0 496 512" style="height:1em;width:0.97em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M245.83 214.87l-33.22 17.28c-9.43-19.58-25.24-19.93-27.46-19.93-22.13 0-33.22 14.61-33.22 43.84 0 23.57 9.21 43.84 33.22 43.84 14.47 0 24.65-7.09 30.57-21.26l30.55 15.5c-6.17 11.51-25.69 38.98-65.1 38.98-22.6 0-73.96-10.32-73.96-77.05 0-58.69 43-77.06 72.63-77.06 30.72-.01 52.7 11.95 65.99 35.86zm143.05 0l-32.78 17.28c-9.5-19.77-25.72-19.93-27.9-19.93-22.14 0-33.22 14.61-33.22 43.84 0 23.55 9.23 43.84 33.22 43.84 14.45 0 24.65-7.09 30.54-21.26l31 15.5c-2.1 3.75-21.39 38.98-65.09 38.98-22.69 0-73.96-9.87-73.96-77.05 0-58.67 42.97-77.06 72.63-77.06 30.71-.01 52.58 11.95 65.56 35.86zM247.56 8.05C104.74 8.05 0 123.11 0 256.05c0 138.49 113.6 248 247.56 248 129.93 0 248.44-100.87 248.44-248 0-137.87-106.62-248-248.44-248zm.87 450.81c-112.54 0-203.7-93.04-203.7-202.81 0-105.42 85.43-203.27 203.72-203.27 112.53 0 202.82 89.46 202.82 203.26-.01 121.69-99.68 202.82-202.84 202.82z"/></svg>Prof. Dr. Jörg Schoder</div>

---
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.blockquote[Fallbeispiel Kundenbindung]

## Kundenbindung & Churn-Analyse: Daten aus dem Retail-Banking

<div id="htmlwidget-e8c24f5e94c6654f35bb" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-e8c24f5e94c6654f35bb">{"x":{"filter":"none","vertical":false,"data":[[1,2,3,4],[15634602,15647311,15619304,15701354],["Hargrave","Hill","Onio","Boni"],[619,608,502,699],["France","Spain","France","France"],["Female","Female","Female","Female"],[42,41,42,39],[2,1,8,1],[0,83807.86,159660.8,0],[1,1,3,2],[1,0,1,0],[1,1,0,0],[101348.88,112542.58,113931.57,93826.63],[1,0,1,0]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th>RowNumber<\/th>\n      <th>CustomerId<\/th>\n      <th>Surname<\/th>\n      <th>CreditScore<\/th>\n      <th>Geography<\/th>\n      <th>Gender<\/th>\n      <th>Age<\/th>\n      <th>Tenure<\/th>\n      <th>Balance<\/th>\n      <th>NumOfProducts<\/th>\n      <th>HasCrCard<\/th>\n      <th>IsActiveMember<\/th>\n      <th>EstimatedSalary<\/th>\n      <th>Exited<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"scrollX":true,"scrollY":"300px","scrollCollapse":true,"autoWidth":false,"columnDefs":[{"width":"100px","targets":"_all"},{"className":"dt-right","targets":[0,1,3,6,7,8,9,10,11,12,13]},{"name":"RowNumber","targets":0},{"name":"CustomerId","targets":1},{"name":"Surname","targets":2},{"name":"CreditScore","targets":3},{"name":"Geography","targets":4},{"name":"Gender","targets":5},{"name":"Age","targets":6},{"name":"Tenure","targets":7},{"name":"Balance","targets":8},{"name":"NumOfProducts","targets":9},{"name":"HasCrCard","targets":10},{"name":"IsActiveMember","targets":11},{"name":"EstimatedSalary","targets":12},{"name":"Exited","targets":13}],"order":[],"orderClasses":false}},"evals":[],"jsHooks":[]}</script>

📝 **Aufgaben:**

1. Importiert die Daten aus Moodle oder unserem [github-Repo](https://github.com/FFHS-EconomicResearch/ER014/blob/main/data/raw/Churn_Modelling.csv) 
2. Identifiziert die Variablentypen. Welche Variable ist als abhängige Variable für eine logistische Regression zur Kundenbindung geeignet?

???
* 14 Variablen mit demografischen und kundenspezifischen Informationen
* mögliches Ziel: Vorhersage der Variable Exited (0 = Kunde bleibt, 1 = Kunde kündigt)

* Daten von [kaggle](https://www.kaggle.com/code/mervetorkan/churn-prediction)
  * RowNumber: corresponds to the record (row) number and has no effect on the output.
  * CustomerId: contains random values and has no effect on customer leaving the bank.
  * Surname: the surname of a customer has no impact on their decision to leave the bank.
  * CreditScore: can have an effect on customer churn, since a customer with a higher credit     * score is less likely to leave the bank.
  * Geography: a customer’s location can affect their decision to leave the bank.
  * Gender: it’s interesting to explore whether gender plays a role in a customer leaving the bank. 
  * Age: this is certainly relevant, since older customers are less likely to leave their bank than younger ones.
  * Tenure: refers to the number of years that the customer has been a client of the bank. Normally, older clients are more loyal and less likely to leave a bank.
  * Balance: also a very good indicator of customer churn, as people with a higher balance in their accounts are less likely to leave the bank compared to those with lower balances.
  * NumOfProducts: refers to the number of products that a customer has purchased through the bank.
  * HasCrCard: denotes whether or not a customer has a credit card. This column is also relevant, since people with a credit card are less likely to leave the bank.
  * IsActiveMember: active customers are less likely to leave the bank.
  * EstimatedSalary: as with balance, people with lower salaries are more likely to leave the bank compared to those with higher salaries.
  * Exited: whether or not the customer left the bank. (0=No,1=Yes)

---
class: left

.blockquote[Fallbeispiel Kundenbindung]

## Kundenbindung & Churn-Analyse: Erste Einblicke

.panelset[
.panel[panel-name[Gesamt]

``` r
# Zusammenfassung Zielvariable
tbl_churn %>% 
  count(Exited) %>% 
  mutate(Prozent=n/sum(n)*100)
```
<div id="htmlwidget-1a6bb81736970e76e3e9" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-1a6bb81736970e76e3e9">{"x":{"filter":"none","vertical":false,"data":[[0,1],[7963,2037],[79.63,20.37]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th>Exited<\/th>\n      <th>n<\/th>\n      <th>Prozent<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"scrollX":true,"scrollY":"300px","scrollCollapse":true,"autoWidth":false,"columnDefs":[{"width":"100px","targets":"_all"},{"className":"dt-right","targets":[0,1,2]},{"name":"Exited","targets":0},{"name":"n","targets":1},{"name":"Prozent","targets":2}],"order":[],"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
]
.panel[.panel-name[Geschlecht]

``` r
tbl_churn %>% group_by(Gender) %>%
                summarise(Anzahl = n(),
                      Churn_Anzahl = sum(Exited),
                      Churn_Rate = mean(Exited) * 100)
```
<div id="htmlwidget-faea6b097469fe376441" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-faea6b097469fe376441">{"x":{"filter":"none","vertical":false,"data":[["Female","Male"],[4543,5457],[1139,898],[25.07153863086071,16.45592816565879]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th>Gender<\/th>\n      <th>Anzahl<\/th>\n      <th>Churn_Anzahl<\/th>\n      <th>Churn_Rate<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"scrollX":true,"scrollY":"300px","scrollCollapse":true,"autoWidth":false,"columnDefs":[{"width":"100px","targets":"_all"},{"className":"dt-right","targets":[1,2,3]},{"name":"Gender","targets":0},{"name":"Anzahl","targets":1},{"name":"Churn_Anzahl","targets":2},{"name":"Churn_Rate","targets":3}],"order":[],"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
]
.panel[.panel-name[Geschlecht & Land]

``` r
tbl_churn %>% group_by(Gender, Geography) %>%
              summarise(Anzahl = n(),Churn_Anzahl = sum(Exited),
                        Churn_Rate = mean(Exited) * 100)
```
<div id="htmlwidget-413b00df4710d4735eef" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-413b00df4710d4735eef">{"x":{"filter":"none","vertical":false,"data":[["Female","Female","Female","Male","Male","Male"],["France","Germany","Spain","France","Germany","Spain"],[2261,1193,1089,2753,1316,1388],[460,448,231,350,366,182],[20.34498009730202,37.55238893545685,21.21212121212122,12.71340355975299,27.81155015197568,13.11239193083573]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th>Gender<\/th>\n      <th>Geography<\/th>\n      <th>Anzahl<\/th>\n      <th>Churn_Anzahl<\/th>\n      <th>Churn_Rate<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"scrollX":true,"scrollY":"200px","scrollCollapse":true,"autoWidth":false,"columnDefs":[{"width":"100px","targets":"_all"},{"className":"dt-right","targets":[2,3,4]},{"name":"Gender","targets":0},{"name":"Geography","targets":1},{"name":"Anzahl","targets":2},{"name":"Churn_Anzahl","targets":3},{"name":"Churn_Rate","targets":4}],"order":[],"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
]
]

---
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.blockquote[Fallbeispiel Kundenbindung]

## Kundenbindung & Churn-Analyse: EDA

📝 **Aufgabe:** Führt den folgenden Code-Block aus und interpretiert die Ergebnisse.

``` r
tbl_churn %>% group_by(Gender) %>% 
              summarise(num_customers = n(), 
                        mean_crScore = round(mean(CreditScore),2), 
                        mean_age = round(mean(Age),2), 
                        mean_tenure = round(mean(Tenure),2), 
                        mean_balance = round(mean(Balance),2), 
                        mean_salary = round(mean(EstimatedSalary),2))
```

<div id="htmlwidget-596e7ee3ee939c261ea8" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-596e7ee3ee939c261ea8">{"x":{"filter":"none","vertical":false,"data":[["Female","Male"],[4543,5457],[650.83,650.28],[39.24,38.66],[4.97,5.05],[75659.37,77173.97],[100601.54,99664.58]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th>Gender<\/th>\n      <th>num_customers<\/th>\n      <th>mean_crScore<\/th>\n      <th>mean_age<\/th>\n      <th>mean_tenure<\/th>\n      <th>mean_balance<\/th>\n      <th>mean_salary<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"scrollX":true,"scrollY":"300px","scrollCollapse":true,"autoWidth":false,"columnDefs":[{"width":"100px","targets":"_all"},{"className":"dt-right","targets":[1,2,3,4,5,6]},{"name":"Gender","targets":0},{"name":"num_customers","targets":1},{"name":"mean_crScore","targets":2},{"name":"mean_age","targets":3},{"name":"mean_tenure","targets":4},{"name":"mean_balance","targets":5},{"name":"mean_salary","targets":6}],"order":[],"orderClasses":false}},"evals":[],"jsHooks":[]}</script>

---
class: left

.blockquote[Fallbeispiel Kundenbindung]

## Kundenbindung & Churn-Analyse: EDA Soziodemografie

📝 **Aufgabe:** Erstellt die Diagramme 1-3 in den panels mit dem **ggplot2**-Paket und interpretiert diese.

.panelset[
.panel[.panel-name[Diagramm 1]
<img src="data:image/png;base64,#02_Fallbeispiel_Churn_files/figure-html/unnamed-chunk-11-1.png" width="45%" style="display: block; margin: auto;" />
]
.panel[.panel-name[Diagramm 2]

<img src="data:image/png;base64,#02_Fallbeispiel_Churn_files/figure-html/unnamed-chunk-12-1.png" width="45%" style="display: block; margin: auto;" />
]
.panel[.panel-name[Diagramm 3]
<img src="data:image/png;base64,#02_Fallbeispiel_Churn_files/figure-html/unnamed-chunk-13-1.png" width="45%" style="display: block; margin: auto;" />
]
]

???

Diagramm 1 alternative
![](data:image/png;base64,#02_Fallbeispiel_Churn_files/figure-html/unnamed-chunk-14-1.png)

![](data:image/png;base64,#02_Fallbeispiel_Churn_files/figure-html/unnamed-chunk-15-1.png)

---
class: left
.blockquote[Fallbeispiel Kundenbindung]

## Kundenbindung & Churn-Analyse: EDA Streudiagramm-Matrix

???

📝 **Aufgabe:** Interpretiert die Kennzahl und das Diagramm

``` r
# Zusammenfassung Zielvariable
tbl_churn %>% 
  count(Exited) %>% 
  mutate(Prozent=n/sum(n)*100)
```

```
## # A tibble: 2 × 3
##   Exited     n Prozent
##    <dbl> <int>   <dbl>
## 1      0  7963    79.6
## 2      1  2037    20.4
```

---
class: left

.blockquote[Fallbeispiel Kundenbindung]

## Weitere Aufgaben

1. Teilt den Datensatz in Trainings- und Testdaten auf (70/30).

2. Trainiert ein logistisches Regressionsmodell auf den Trainingsdaten und erstellt Vorhersagen für die Testdaten.

3. Erstellt eine Confusion Matrix und berechnet die Accuracy, Precision, Recall und F1-Score.

4. Diskutiert, welche dieser Metriken in diesem geschäftlichen Kontext am wichtigsten sein könnte und warum.

5. Erstellt die ROC-Kurve für Ihr Modell, berechnet den AUC-Wert und interpretiert ihn.

6. Experimentiert mit verschiedenen Schwellenwerten und beobachtet, wie sich Precision und Recall ändern.

---
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# Schönen Feierabend!

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